Category: DSP

All About Digital Signal Processing…

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DSP kecerdasan artifisial

Kecerdasan Artifisial dalam Pengolahan Data Seismik

sWebinar yang diselenggarakan pada Jumat, 1 Oktober 2021 oleh Bisa.ai menjelaskan secara singkat terkait dengan penggunaan Kecerdasan Artifisial pda pengolahan data seismik.

Mengawali webinar saya menampiokan distribusi spasial sebaran gempabumi di Indonesia, jangan kaget, jangan panik (perhatikan gambar berikut)

Selanjutnya saya berbicara tentang apa itu Kecerdasan Artifisial, kaitannya dengan Machine Learning dan Deep Learning. Bberapa penjelasan singkat terkait dengan aplikasi Machine Learning pada pengolahan data-data seismik, baik gempa bumi maupun vulkanik, dalam beberapa paper terkait. Selengkapnya silahkan menyaksikan VIDEO-nya (klik aja video-nya).

Semoga bermanfaat dan terima kasih.

Categories
DSP

Implementasi Sinyal dan Speech Processing

WORKSHOP – Riset dan Implementasi di Bidang Speech, Signal dan Music Processing – Sabtu, 8 Mei 2021

Implementasi Sinyal dan Speech Processing – Dr. Agfianto Eko Putro, M. Si.

== silahkan klik tautan di atas untuk menyaksikan video-nya ==

Categories
DSP FPGA kecerdasan artifisial

Arah Aktivitas/Penelitian Kecerdasan Artifisial Indonesia

Sosialisasi Pembentukan Pengurus dan Anggota Asosiasi Kecerdasan Buatan DAY 2 – Minggu, 25 april 2021.

Pada kesempatan ini saya coba menjelaskan dengan singkat konten stranas Kecerdasan Artifisial di Indonesia, khususnya terkait dengan arah aktivitas atau penelitian Kecerdasan Artifisial di Indonesia. Silahkan untuk lebih jelasnya menyaksikan tayangan berikut…

Arah Aktivitas/Penelitian Kecerdasan Artifisial Indonesia

== klik tautan diatas untuk menyaksikan video-nya ==

Categories
DSP FPGA

Digital Signal Processing and Deep Learning – Bisa.AI dan APTIKOM DIY

Rekaman live Sabtu, 4 Juli 2020

Pada Roundtable ini akan membahas mengenai Digital Signal Processing dan Deep Learning dengan narasumber:

  • Dr. Agfianto Eko Putra (Dosen DIKE, Fak. MIPA, Universitas Gadjah Mada, Ketua Aptikom Daerah Istimewa Yogyakarta) — Pembahasan: digital signal processing untuk kasus speech signal dan seismic dengan MATLAB;
  • M. Octaviano Pratama, M.Kom (Chief Scientist BISA AI) — Pembahasan: klasifikasi low level dan high level speech feature dengan Deep Learning;
klik disini untuk link YOUTUBE-nya . Terima kasih dan semoga bermanfaat.
Categories
DSP satelit

SEMILINS Edisi Juni 2020: Peran Satelit di Era Industri 4.0

Seminar ELINS edisi Juni 2020 dengan topik “Peran Satelit di Era Industri 4.0” bersama Anggoro Kurnianto Widyawan, Ph.D. – Direktur Operasi PT Telkomsat dan Dr. Agfianto Eko Putra, M.Si. – Dosen dan Peneliti Elektronika dan Instrumentasi UGM.

Peran Satelit di Era Industri 4.0

== silahkan klik tautan diatas untuk menyaksikan video langsung ke topik satelit-nya ==

Categories
DSP

Improvement of accuracy in batik image classification due to scale and rotation changes using M2ECS-LBP algorithm

Rangkuti, A.H., Harjoko, A., and Putra, A.E.

This research evolves feature extraction algorithms in overcoming difficulties in classifying batik images that encounter changes in scale and rotation. the algorithm is multiscale and multilevel extended center symmetric local binary pattern (M2ECS-LBP). In utilizing this algorithm using several types of windows to obtain optimal feature extraction results, ranging from the size of 6×6, 9×9, 12 x 12 and 15×15 or a combination of several windows. However, for the use of algorithm carried out sequentially, it also requires a special strategy to obtain optimal image feature extraction results to support performance accuracy in the classification. The results of classification accuracy using K-Nearest neighborhood had reached up until the percentage to 78,4 – 81.7 percent of the image undergoing changes in scale and rotation. However, if the batik image undergoes a change in scale but the rotation is the same then the accuracy percentage can reach 98-99 percent. This algorithm is a very powerful breakthrough with lighter computing techniques than other algorithms. This research can be continued to recognize moving images, expected with maximum accuracy.

[click here]

Categories
DSP

Depth Limitation and Splitting Criteria Optimization on Random Forest for Efficient Human Activity Classification

Random Forest (RF) is known as one of the best classifiers in many fields. They are parallelizable, fast to train and to predict, robust to outlier, handle unbalanced data, have low bias, and moderate variance. Apart from these advantages, there are still opportunities to increase RF efficiency. The absence of recommendations regarding the number of trees involved in RF ensembles could make the number of trees very large. This can increase the computational complexity of RF. Recommendations for not pruning the decision tree further aggravates the condition. This research attempts to build an efficient RF ensemble while maintaining its accuracy, especially in problem activity. Data collection is performed using an accelerometer sensor on a smartphone device. The data used in this research are collected from five peoples who perform 11 different activities. Each activity is carried out five times to enrich the data. This study uses two steps to improve the efficiency of the classification of the activity: 1) Optimal splitting criteria for activity classification, 2) Measured pruning to limit the tree depth in RF ensemble. The first method in this study can be applied to determine the splitting criteria that are most suitable for the classification problem of activities using Random Forest. In this case, the decision model built using the Gini Index can produce the highest accuracy. The second method proposed in this research successfully builds less complex pruned-tree without reducing its classification accuracy. The research results showed that the method applied to the Random Forest in this study was able to produce a decision model that was simple but yet accurate to classify activity.

[https://dx.doi.org/10.14569/IJACSA.2019.0100658]

Categories
DSP

Evaluation of Suitability of Voice Reading of Al-Qur’an Verses Based on Tajwid Using Mel Frequency Cepstral Coefficients (MFCC) and Normalization of Dominant Weight (NDW)

Heriyanto, Hartati and Putra, 2018

The recitation of the Qur’an has its own uniqueness, among others having a special rule in reading and pronunciation, which is called tajwid science. At the time of the Qur’an is recited, there are often mistakes due to the limitations of knowledge of Tajwid. Therefore, the availability of tools to facilitate in checking the appropriateness of recitation is very much needed by those who recite the Qur’an and face limitations in understanding the science of tajwid. Checking the Qur’an reading is a problem that must be solved according to the rules. So far, voice identification studies have problems with feature extraction, compatibility or suitability testing, and accuracy. The issue of feature extraction, suitability, and impermanence testing have been improved in this study, which consists of two stages. The first stage is the extraction of the sound character of the Qur’an reading and the second stage is the testing of the conformity of the Qur’anic recitation and accuracy. In the first stage feature extraction is handled using MFCC and Normalization of Dominant Weight (NDW). Characteristics of reading the Qur’an as reference table is taken from one reader of Al-Qur’an who has competence in the field of science tajwid, for sampling 5-7 people as a source for testing. The process of the second stage of conformity testing of Qur’an reading is done starting from filtering, sequential multiplication of reference table and Conformity Uniformity Pattern (CUP). The sample of reading conformity test is taken from 11 Qur’anic letters containing 8 reading laws and 886 records. The test is performed on the dominant frame, the number of cepstral coefficient and the number of frames. The reading conformance test provides an average accuracy of 91.37% on the nine dominant frames. The test for the number of cepstral coefficients in the c-23 can be an average of 96.65%, while the number of frames on the F-10 is the best average of 96.65%.

[https://doi.org/10.14738/aivp.62.4268]

Categories
DSP

Analysis of 2006 Merapi Eruption Data Based on Continous Wavelet Transform, Wavelet Decomposition and Correlation

by Agfianto Eko PUTRA, Wiwit SURYANTO, Agung Nugraha SULISTYANA

Seismic data analysis of the 2006 Merapi volcano eruption has been carried out using the Continuous Wavelet Transform (CWT) and the Wavelet-based Decomposition and Correlation (WAVEDECOR) combined with the Fast Fourier Transform (FFT). The CWT is used to show the frequency pattern of the event while the WAVEDECOR is used to denote the frequency band of the signal. The CWT and the WAVEDECOR are supported by the FFT to ensure the dominant frequency of the observed signals. The results show that visual patterns and dominant frequency distribution of certain events, including the VT-A, the Low Frequency (LF), the VT-B, tremor, multiphase and lava avalanche. The result from this analysis was then compared with related eruption signal of Merapi in 1996 to determine the pattern similarity. The comparison results show almost identical results for dominant frequencies in VT-A events as well as MP events. The findings in the VT-B event showed that the dominant frequency pattern was slightly different from the 1996 data which showed at medium to high-frequency while for the 2006 data showed only at a medium frequency.

[click here]

Categories
DSP

Movement Direction Estimation on Video using Optical Flow Analysis on Multiple Frames

Achmad Solichin, Agus Harjoko, and Agfianto Eko Putra

This study proposed a model for determining the movement direction of the object based on the optical flow features. To increase the speed of computational time, optical flow features derived into a Histograms of Oriented Optical Flow (HOOF). We extracted them locally on the grid with a certain size. Moreover, to determine the movement direction we also analyzed multiple frames at once. Based on the experiment results, showing that the value of accuracy, precision, and recall of the movement detection is good, amounting to 93% for accuracy, 73.07% for precision and 84.25% for recall. Furthermore, the results of testing using the best parameter shows the value of accuracy of 98.1%, 35.6% precision, 41.2% recall, and direction detection error rate (DDER) 25,28%. The results of this study are expected to provide benefits in video analysis studies such as riots detection and abnormal movement in public places.

[click here]